A hybrid method combining Markov prediction and fuzzy classification for driving condition recognition

Haiming Xie*, Guangyu Tian, Guangqian Du, Yong Huang, Hongxu Chen, Xi Zheng, Tom H. Luan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


Driving condition adaptive control is an effective vehicle fuel-saving technique, and the key challenge lies in improving the recognition accuracy of current driving condition. The state-of-the-art approach is based on recognizing historical driving data with a fixed length sliding window to detect current driving condition. However, few research has been conducted to directly recognize the occurring micro-trip (a speed time series between two starts). That is because at the beginning stage of an occurring micro-trip, its known speed time series is too short to be correctly recognized. In this paper, we addressed this issue by proposing a hybrid method for the occurring micro-trip recognition, and two efforts are made to improve recognition accuracy. First, a hybrid recognition procedure is proposed, which combines the Markov chain prediction model and the fuzzy classification model. Second, a statistic approach is proposed to estimate the best time to switch between above-mentioned two models to achieve higher accuracy in detecting current driving condition. Our evaluation results on real-world driving data show that our proposed solution has better accuracy than the state-of-the-art approach.

Original languageEnglish
Article number8456631
Pages (from-to)10411-10424
Number of pages14
JournalIEEE Transactions on Vehicular Technology
Issue number11
Publication statusPublished - 1 Nov 2018


  • Driving condition recognition
  • Fuzzy classification
  • Hybrid recognition
  • Markov prediction
  • Micro-trip


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